Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
trl
rloo
conversational
text-generation-inference
Instructions to use SaminSkyfall/rloo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaminSkyfall/rloo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SaminSkyfall/rloo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SaminSkyfall/rloo") model = AutoModelForCausalLM.from_pretrained("SaminSkyfall/rloo") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SaminSkyfall/rloo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SaminSkyfall/rloo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SaminSkyfall/rloo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SaminSkyfall/rloo
- SGLang
How to use SaminSkyfall/rloo with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "SaminSkyfall/rloo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SaminSkyfall/rloo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "SaminSkyfall/rloo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SaminSkyfall/rloo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SaminSkyfall/rloo with Docker Model Runner:
docker model run hf.co/SaminSkyfall/rloo
Training in progress, step 31
Browse files- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- added_tokens.json +1 -2
- special_tokens_map.json +1 -1
- tokenizer.json +2 -2
- tokenizer_config.json +1 -10
- training_args.bin +1 -1
adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"v_proj",
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"down_proj",
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"q_proj",
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"gate_proj",
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"up_proj",
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"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"k_proj",
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"o_proj",
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"v_proj",
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"down_proj",
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"up_proj",
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"gate_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 80792456
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added_tokens.json
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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"[PAD]": 151669
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}
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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special_tokens_map.json
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"single_word": false
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},
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"pad_token": {
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"content": "
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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tokenizer.json
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size 11422934
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tokenizer_config.json
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"151669": {
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"content": "[PAD]",
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"normalized": false,
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"single_word": false,
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"special": true
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},
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"additional_special_tokens": [
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"errors": "replace",
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"extra_special_tokens": {},
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"model_max_length": 131072,
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"pad_token": "
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"additional_special_tokens": [
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"errors": "replace",
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"extra_special_tokens": {},
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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training_args.bin
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size 6609
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version https://git-lfs.github.com/spec/v1
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oid sha256:0896d5e9f2f47338a1acae78d5fc02f6f0528b622a3c60e6ecc62b8e0122346c
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size 6609
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